Handling Consumer Uncertainty in Energy Reduction Bid Execution in the Spot Local Flexibility Market

In the Spot Local Flexibility Market (SLFM), which operates close to real-time, aggregators procure consumer energy flexibility to manage distribution-level congestion. Consumers submit flexibility bids based on their load prediction models and personal preferences. Aggregators then execute a single-sided combinatorial auction aligned with the Distribution System Operator’s (DSO) flexibility requirements. However, uncertainties in consumer load forecasts can affect bid execution, potentially resulting in penalties for aggregators when delivered flexibility deviates from committed levels. This work proposes a stochastic programming approach to address such uncertainties within the auction framework. By adaptively learning each consumer’s uncertainty distribution, aggregators can select bids that better accommodate this variability. The proposed method enhances the accuracy of energy reduction reporting, improves the DSO’s visibility into real-time flexibility, and reduces penalties for aggregators. A comparative analysis with deterministic market clearing demonstrates that incorporating uncertainty into the SLFM increases aggregator profits by an average of 50.88%, depending on scenario initial conditions.